In recent years, nuclear medicine procedures have been increasingly used to trace distributions of various substances in the human body. Typically, radioisotope-tagged pharmaceuticals (radiopharmaceuticals) are administered to a subject, which may be a human or an animal, for example. Common imaging techniques, such as Single Photon Emission Computed Tomography (SPECT), for example, are then used to measure radiation from the radiopharmaceuticals to produce data and/or images representing the three-dimensional distribution of the radiopharmaceuticals in the subject's body or in a particular organ or region thereof. Scans of this nature may be used for clinical diagnosis of sick patients, for example.
Current SPECT visualization is generally performed under the assumption that the spatial distribution of the radiopharmaceuticals in the subject's body is static throughout the duration of the scan, typically 20–25 minutes. However, physiological processes in the human body are usually dynamic, therefore many organs, such as the kidney and heart, for example, tend to show significant changes in activity distribution over time due to uptake (“washin”) or due to “washout” of the radiopharmaceuticals.
Moreover, it is presently thought that the rates of infusion and subsequent extraction of such radiopharmaceuticals from the tissue provide good indications of organ function. Therefore, while current SPECT technology and methods are useful for measuring a static distribution of a substance, they are not suitable for measuring dynamic physiological processes, such as kidney or heart function, for example, which require dynamic data representing the “washin” or “washout” rates of the substance.
In other words, measurement of physiological processes requires four-dimensional dynamic data representing both the spatial and temporal distributions of the radiopharmaceutical.
Other technologies exist for producing dynamic data of this general nature, however, such technologies suffer from a number of disadvantages.
One technique involves fast rotations around the subject with a SPECT camera. However, due to the relatively high speed at which the camera moves, very few counts are received at any given camera location. This method therefore produces statistically unreliable data, resulting in poor spatial resolution. Additionally, this technique is generally incompatible with the majority of transmission scan-based attenuation correction methods. Equipment for performing this technique is rarely available in hospitals.
Ring SPECT devices may also suffer from statistically unreliable data. Typically, each of the detectors forming the ring receives a relatively low number of counts, resulting in a low signal to noise ratio. Ring SPECT devices are also expensive and are typically not available in hospitals, being used mainly for research.
Similarly, Positron Emission Tomography (PET) devices are also prohibitively expensive, and are not available in many hospitals. PET equipment is much more complex than SPECT equipment, and the PET technique requires a cyclotron in proximity to or in the hospital in which the PET equipment is housed. In addition, PET and SPECT use different isotopes and radiopharmaceuticals, and are therefore complementary. In addition, data analysis may be cumbersome with the PET technique, wherein typically 64 or 128 projections or input images are required to produce each reconstructed image.
Planar imaging is commonly available in hospitals. However, this technique generally involves acquiring a series of fast images, typically 1–10 seconds, which sometimes results in a low signal-to-noise ratio. The camera generally remains at a fixed position, resulting in limited spatial resolution, an inability to produce three-dimensional images of the object of interest and an inability to perform proper attenuation correction.
More generally, existing dynamic analysis methods generally assume that the activity of each three-dimensional pixel or “voxel” of the area of interest behaves according to a particular functional model, such as exponential decay or biexponential decay, for example. One such method, Factor Analysis of Dynamic Structures (FADS), proposes a “menu” of possible dynamic profiles or basis functions, sometimes referred to as “factors”, which may include exponential or biexponential functions for example. FADS attempts to fit a linear combination of such basis functions to the measured data. However, the exponential or other functional models imposed on the data may not apply in some situations, and in such a case this technique may fail. This method may also fail due to inherent numerical instabilities if the number of dynamic pixels is high. In addition, existing dynamic analysis methods are cumbersome and time-consuming, and often require as much as one to two hours or more of computing time to complete the data analysis using contemporary desktop computers.
Accordingly, there is a need for a way to produce a representation of a measurable property which varies in time and space. More particularly, there is a need for ways to produce an image representing changes in radioactivity in an object and to use standard nuclear medicine equipment currently available in most hospitals, to quickly produce dynamic data representing physiological processes over time, without sacrificing the resolution or reliability of the data, and without requiring the data to satisfy any pre-determined form of function.